Sequential Sentence Matching Network for Multi-turn Response Selection in Retrieval-based Chatbots
This work addresses the challenge of improving matching accuracy in multi-turn chatbots for researchers and developers, though it is incremental as it builds on existing context-response matching methods.
The paper tackles the problem of multi-turn response selection in retrieval-based chatbots by proposing a sequential sentence matching network (S2M) that leverages sentence-level semantic information, achieving state-of-the-art performance with significant improvements on three public datasets.
Recently, open domain multi-turn chatbots have attracted much interest from lots of researchers in both academia and industry. The dominant retrieval-based methods use context-response matching mechanisms for multi-turn response selection. Specifically, the state-of-the-art methods perform the context-response matching by word or segment similarity. However, these models lack a full exploitation of the sentence-level semantic information, and make simple mistakes that humans can easily avoid. In this work, we propose a matching network, called sequential sentence matching network (S2M), to use the sentence-level semantic information to address the problem. Firstly and most importantly, we find that by using the sentence-level semantic information, the network successfully addresses the problem and gets a significant improvement on matching, resulting in a state-of-the-art performance. Furthermore, we integrate the sentence matching we introduced here and the usual word similarity matching reported in the current literature, to match at different semantic levels. Experiments on three public data sets show that such integration further improves the model performance.